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Title: JRmGRN: joint reconstruction of multiple gene regulatory networks with common hub genes using data from multiple tissues or conditions
Abstract Motivation

Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges.

Results

In this paper, we proposed a new graphic Gaussian model for joint reconstruction of multiple gene regulatory networks (JRmGRN), which highlighted hub genes, using gene expression data from several tissues/conditions. Under the framework of Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log likelihood function. We formulated it as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. The performance of JRmGRN was first evaluated with synthetic data and the results showed that JRmGRN outperformed several other methods for reconstruction of GRNs. We also applied our method to real Arabidopsis thaliana RNA-seq data from two light regime conditions in comparison with other methods, and both common hub genes and some conditions-specific hub genes were identified with higher accuracy and precision.

Availability and implementation

JRmGRN is available as a R program from: https://github.com/wenpingd.

Supplementary information

Supplementary data are available at Bioinformatics online.

 
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NSF-PAR ID:
10393407
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Bioinformatics
Volume:
34
Issue:
20
ISSN:
1367-4803
Page Range / eLocation ID:
p. 3470-3478
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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